#4995. Deep learning-based container throughput forecasting: a triple bottom line approach

July 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Industrial Relations;
Strategy and Management;
Industrial and Manufacturing Engineering;
Computer Science Applications;
Management Information Systems;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting. A novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test. The result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. A novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).
Keywords:
Container throughput; Forecasting; LSTM; Machine learning; Principal component analysis; Triple bottom line

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